A Comparative Study of the Kalman Filter and the LSTM Network for the Remaining Useful Life Prediction of SOFC

Chuang Sheng, Yi Zheng, Rui Tian (), Qian Xiang, Zhonghua Deng, Xiaowei Fu and Xi Li ()
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Chuang Sheng: School of Artificial Intelligence and Automation, Key Laboratory of Image Processing and Intelligent Control of Education Ministry, Huazhong University of Science and Technology, Wuhan 430074, China
Yi Zheng: School of Artificial Intelligence and Automation, Key Laboratory of Image Processing and Intelligent Control of Education Ministry, Huazhong University of Science and Technology, Wuhan 430074, China
Rui Tian: School of General Aviation, Jingchu University of Technology, Jingmen 448000, China
Qian Xiang: School of Artificial Intelligence and Automation, Key Laboratory of Image Processing and Intelligent Control of Education Ministry, Huazhong University of Science and Technology, Wuhan 430074, China
Zhonghua Deng: School of Artificial Intelligence and Automation, Key Laboratory of Image Processing and Intelligent Control of Education Ministry, Huazhong University of Science and Technology, Wuhan 430074, China
Xiaowei Fu: Science and Technology Research Institute, Shenzhen Huazhong University, Shenzhen 518055, China
Xi Li: School of Artificial Intelligence and Automation, Key Laboratory of Image Processing and Intelligent Control of Education Ministry, Huazhong University of Science and Technology, Wuhan 430074, China

Energies, 2023, vol. 16, issue 9, 1-16

Abstract: The solid oxide fuel cell (SOFC) system is complicated because the characteristics of gas, heat, and electricity are intricately coupled. During the operation of the system, problems such as frequent failures and a decrease in the stack’s performance have caused the SOFC system to work less well and greatly shortened the SOFC’s practical life. As such, it is essential to accurately forecast its remaining useful life (RUL) to make the system last longer and cut down on economic losses. In this study, both model-based and data-driven prediction methods are used to make predictions about the RUL of SOFC. First, the linear degradation model of the SOFC system is established by introducing degradation resistance as the index of health status. Using the Kalman filtering (KF) method, the health status of SOFC is evaluated online. The results of the health state estimation indicated that the KF algorithm is accurate enough to provide a good basis for the model-based RUL prediction. Then, a long short-term memory (LSTM) network-recursive (data-driven) method is presented for RUL prognostics. The multi-step-ahead recursive strategy of updating the network state with actual test data improves the prediction accuracy. Finally, a comparison is made between the LSTM network prediction approach suggested and the model-based KF prognostics. The results of the experiments indicate that the LSTM network is more suitable for RUL prediction than the KF algorithm.

Keywords: SOFC; remaining useful life prediction; Kalman filtering; long short-term memory network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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